Predictive model for protein function using modular neural approach

Authors:
Doosung Hwang;Ungmo Kim;Jaehun Choi;Jeho Park;Janghee Yoo
Affiliations:
Department of Computer Science, Dankook University, Chungnam, Korea;Department of Computer Engineering, Sungkyunkwan University, Kyounggi, Korea;Electronics and Telecommunications Research Institute, Daejeon, Korea;Department of Computer Science, Dankook University, Chungnam, Korea;Electronics and Telecommunications Research Institute, Daejeon, Korea
Venue:
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Year:
2005

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Abstract

As interest within bioinformatics has been vastly increased, efforts to predict functional role of proteins have been made using diverse approaches. In this paper, we discuss a protein function prediction method that utilizes protein molecular information including protein interaction data. The proposed method takes the given problem into account as a K-class classification problem and resolves the new problem by using a modular neural network based predictive approach. The simulation demonstrates that the proposed approach predicts the functional roles of Yeast proteins with unknown functional knowledge and is competitive to the other methodologies in KDD Cup 2001 competition.